The present invention relates to video processing and, more particularly, to a method and device for real-time two dimensional filtering of video signals.
Video cameras are familiar in their industrial (e.g., commercial television) and consumer (home video) applications. A video camera renders the scene being photographed as a video image, which is an electrical voltage as a function of time. As such, it is a one-dimensional signal that represents the two-dimensional line-scanned scene. The two-dimensionality of the scene is represented in the video image by synchronization pulses, which allow a two-dimensional representation of the scene to be reconstructed from the one-dimensional video image.
Video images suffer from two kinds of distortion. One kind is inherently two-dimensional, associated with factors such as atmospheric blur, image motion, vibration blur and defocusing. The other kind is inherently one-dimensional and includes noise imposed on the video image by the electronic system of the camera. One-dimensional distortions can be suppressed by one-dimensional techniques. Two-dimensional distortions suppression requires two-dimensional techniques.
Because analog techniques operate on the video signal itself, which is inherently one dimensional, the usual approaches to picture enhancement and restoration are digital techniques, include digital signal processing (DSP) and digital filtering. The development of DSP microprocessors has made DSP the method of choice in recent years. Unfortunately, real time DSP requires relatively expensive DSP microprocessors.
Consider, for example, a typical DSP sequence performed on a 512.times.512 pixel video frame. The sequence includes a forward 2D FFT (3.15 million floating point operations), multiplication by a restoration filter in the transform domain (0.2 million floating point operations), and an inverse 2D FFT (another 3.15 million floating point operations). Adding to this 20% overhead for branches in program execution and 20% overhead associated with parallelization gives a total of about 10 million floating point operations to process one video frame. Convolutional processing generally requires even more operations than FFT processing: a 4.times.4 convolution of a 512.times.512 pixel frame requires 8.38 million floating point operations. At a standard rate of 25 frames per second (Europe) or 30 frames per second (USA), this means that a DSP microprocessor must perform at speeds in excess of 250 Mflops just to perform the calculations. If the operations needed to transfer the data to and from the processor are included, it turns out that the processor must perform faster than 400 Mflops if programmed in assembler language and faster than 600 Mflops if programmed in a high level language such as C. Typical moderately priced DSP microprocessors do not achieve these speeds.
DSP has other disadvantages. The millions of floating point operations applied to a single video frame introduce numerical roundoff error. The operation count cited above is for a power-of two FFT. The pixel dimensions of typical video frames often are not powers of two, so either the digitized frames must be padded with zeros or an even slower DFT (Digital Fourier Transform) must be performed. These disadvantages would be obviated if two-dimensional picture enhancement could be done by analog means.
There is thus a widely recognized need for, and it would be highly advantageous to have, an at least partly analog method and device for two-dimensional video picture enhancement.